Job Location
Santa Clara, CA - Onsite
Employment Type
Full-time
Job Description
Must Have Technical/Functional Skills
- Data, AI and conversational analytics - core of the role
- Strong consulting and solution architecture experience spanning modern data platforms, Data Engineering, Data Management, analytics, Generative AI and conversational analytics.
- Working knowledge of Data Engineering using Python, SQL, Spark / PySpark, ETL / ELT, data pipelines, batch and streaming patterns, APIs, Kafka and cloud-native orchestration services.
- Working knowledge of Data Management, including data architecture, lakehouse and medallion patterns, Data Mesh, Master Data Management, metadata, cataloging, data quality, lineage, security, privacy and governance.
- Hands-on understanding of conversational analytics and natural-language access to enterprise data, including text-to-SQL, semantic layers, RAG, enterprise search, chat-based BI, insight generation and responsible access controls.
- Deep understanding of GenAI engineering: RAG over enterprise data, prompt and context engineering, embeddings and vector stores such as Pinecone, Weaviate or Milvus, model selection and LLM fine-tuning using LoRA / QLoRA.
- Practical experience with agentic AI patterns including planning, tool use, function calling, memory and multi-agent orchestration using LangChain, LangGraph, Semantic Kernel or equivalent.
- Knowledge of classical ML and data science using Python, PyTorch / TensorFlow and scikit-learn, including feature engineering, model evaluation and interpretability.
- Production awareness across DataOps, MLOps and LLMOps: CI/CD, testing, observability, model and data monitoring, drift, cost management, Docker and Kubernetes.
Cloud data and AI platform foundation
- Strong expertise in at least one cloud data and AI stack, with familiarity in a second: Google Cloud (BigQuery, Dataflow, Dataproc, Dataplex, Vertex AI), Azure (Data Factory, Databricks, Fabric, Purview, Azure ML, Azure OpenAI) or AWS (Glue, EMR, Redshift, SageMaker, Bedrock).
- Experience with Databricks and/or Snowflake, including lakehouse or warehouse architecture, data engineering, governance and AI capabilities such as Mosaic AI or Cortex.
- Ability to design an AI-ready data estate with scalable ingestion, transformation, semantic and serving layers, backed by data quality, lineage, governance and observability.
Consulting, CoE leadership and solutioning
- Proven experience leading or building a Data and AI practice, CoE or capability pod, defining offerings and assets, setting architecture standards, and mentoring data, analytics and AI teams.
- Strong customer-facing track record in discovery and design workshops, use-case prioritization, current-state assessment, roadmap creation and executive presentations.
- Ownership of RFP / RFI and proactive Data and AI proposals covering solution approach, target architecture, estimation, delivery model, commercials inputs and orals.
- Experience leading initiatives from assessment and PoC through pilot and production with distributed onsite-offshore teams.
Domain context
- Experience with high-technology customers including hyperscalers, software and platform companies, semiconductor organizations and consumer-electronics enterprises.
Roles & Responsibilities
We are seeking a Data and AI Consultant for a newly formed Data & Analytics unit serving high-technology customers on the US West Coast. This is a senior, customer-facing role that combi nes advisory and solutioning leadership with practical knowledge across Data Engineering, Data Management, conversational analytics, advanced analytics and AI. The consultant will shape account-level Data and AI agendas, identify opportunities, develop roadmaps and work with CoE and delivery teams to take solutions into production.
Key Responsibilities
- Data and AI advisory: Build trusted relationships with customer data, analytics, AI and platform leaders; assess business and technology priorities; and define practical transformation roadmaps.
- Data Engineering and platform solutioning: Shape modern data architectures and engineering solutions covering ingestion, transformation, lakehouse / warehouse, streaming, integration and data serving.
- Data Management: Advise on governance, metadata, cataloging, quality, lineage, Master Data Management, security and privacy to make enterprise data trusted and AI-ready.
- Conversational analytics: Identify and solution natural-language analytics opportunities such as text-to-SQL, chat-based BI, semantic search, RAG-enabled insight assistants and conversational workspaces, with appropriate governance and access controls.
- AI and GenAI solutioning: Lead discovery and design for ML, GenAI and agentic use cases; assess feasibility and value; define target architecture; and establish evaluation, monitoring and Responsible AI guardrails.
- Customer workshops: Plan and facilitate Data and AI discovery sessions, current-state assessments, architecture workshops and use-case prioritization discussions, converting outcomes into a sequenced roadmap and qualified pipeline.
- Proposals and presales: Own or contribute to RFP, RFI and proactive proposals, including solution approach, reference architecture, estimates, delivery model, staffing and pricing inputs; present and defend recommendations to business and technical stakeholders.
- Offerings and assets: Convert recurring customer needs into reusable accelerators, PoVs, demos, reference architectures and playbooks with Data and AI CoEs and alliance partners.
- Delivery governance: Guide programs from assessment and PoC through pilot and production; review key architecture and engineering deliverables; and ensure quality, security, observability, cost control and business outcomes.
- Capability building: Define skill priorities across Data Engineering, Data Management, analytics and AI; mentor consultants, architects and engineers; and maintain a current market and technology point of view.
Generic Managerial Skills, If Any
- Ability to lead a technical capability without formal line authority setting direction, standards and priorities across pods and accounts.
- Strong consultative and communication skills credible with principal AI engineers and confident presenting to executive stakeholders; willing and able to travel regularly to customer locations across the US West Coast.
- Experience coordinating across geographies and time zones with offshore delivery and CoE teams; mentoring and growing technical talent.
- Comfortable in an early-stage unit self-directed, pragmatic and able to build structure where none yet exists.